In this paper, we describe a post-hoc explanation-by-example approach to eXplainable AI (XAI), where a black-box, deep learning system is explained by reference to a more transparent, proxy model (in this situation a case-based reasoner), based on a feature-weighting analysis of the former that is used to find explanatory cases from the latter (as one instance of the so-called Twin Systems approach). A novel method (COLE-HP) for extracting the feature-weights from black-box models is demonstrated for a convolutional neural network (CNN) applied to the MNIST dataset; in which extracted feature-weights are used to find explanatory, nearest-neighbours for test instances. Three user studies are reported examining people's judgements of right and wrong classifications made by this XAI twin-system, in the presence/absence of explanations-by-example and different error-rates (from 3-60%). The judgements gathered include item-level evaluations of both correctness and reasonableness, and system-level evaluations of trust, satisfaction, correctness, and reasonableness. Several proposals are made about the user's mental model in these tasks and how it is impacted by explanations at an item- and system-level. The wider lessons from this work for XAI and its user studies are reviewed.

在本文中，我们描述了一个事后的解释按示例的方法来解释的AI（XAI），其中一个黑盒子，深度学习系统通过引用解释更加透明，代理模型（在这种情况下一个病例基于推理器），基于对前者的特征加权分析，该特征加权分析用于从后者中寻找解释性案例（作为所谓的Twin Systems的一个实例）方法）。针对应用于MNIST数据集的卷积神经网络（CNN），展示了一种从黑盒模型中提取特征权重的新方法（COLE-HP）；其中提取的特征权重用于查找测试实例的解释性最近邻。据报道，有三项用户研究检查了人们对这个XAI双系统进行的正确与错误分类的判断，并以示例为例进行解释，并且错误率不同（3-60％）。收集的判断包括对项目的正确性和合理性评估，以及对信任度，满意度，正确性和合理性的系统评估。针对这些任务中用户的心理模型，以及在项目和系统级别的解释如何影响它，提出了一些建议。